class_docstr: A global average pooling block.
  The block expects the input tensors to have the from (batch-dim, channel-dim, row-dim, column-dim).
  
  :param in_keys: One key identifying the input tensors.
  :param out_keys: One key identifying the output tensors.
  :param in_shapes: List of input shapes.
docstr: implementation of :class:`~maze.perception.blocks.shape_normalization.ShapeNormalizationBlock` interface
          
docstr_tok: ["implementation","of",":","class",":","`","~maze.perception.blocks.shape_normalization.ShapeNormalizationBlock","`","interface"]
code: def normalized_forward(
      self, block_input: Dict[str, torch.Tensor]
  ) -> Dict[str, torch.Tensor]:
      """implementation of :class:`~maze.perception.blocks.shape_normalization.ShapeNormalizationBlock` interface"""
  
      # check input tensor
      input_tensor = block_input[self.in_keys[0]]
      assert input_tensor.ndim == self.in_num_dims[0]
  
      # forward pass
      output_tensor = (
          self.net(input_tensor).squeeze(dim=-1).squeeze(dim=-1)
      )
  
      # check output tensor
      assert output_tensor.ndim == self.out_num_dims[0]
  
      return {self.out_keys[0]: output_tensor}
  
file_path: maze/maze/perception/blocks/general/gap.py
imports: from typing import Union, List, Sequence, Dict
  import torch
  from torch import nn
  from maze.core.annotations import override
  from maze.perception.blocks.shape_normalization import ShapeNormalizationBlock
label_desc: Use image node_id 2 for calling the GlobalAveragePoolingBlock obj's underlying member method code with example usage: obj.normalized_forward(block_input) and returns: dict